Instructor: In this lesson, we will learn about
ethical and governance considerations.
Ethical and governance considerations are used to establish
frameworks and guidelines to ensure AI,
or artificial intelligence,
is developed and used responsibly.
And that it aligns with societal values
and organizational principles.
Ethical and governance considerations
include the ethical governance of AI,
and organizational policies on the use of AI.
The ethical governance of AI refers to the structures
and practices put in place
to guide the ethical deployment of AI.
Organizational policies on the use of AI
are specific rules and guidelines created by companies
to govern how AI is implemented within their organization.
Local organizational policies ensure that AI use
aligns with both legal requirements
and an organization's ethical standards.
Let's learn more about the ethical governance
of artificial intelligence, and organizational policies
on the use of artificial intelligence.
First, we have the ethical governance of AI,
or artificial intelligence.
The ethical governance of AI establishes structures
and practices to ensure AI is used responsibly,
aligning with societal values.
This governance prioritizes fairness, transparency,
and accountability in AI applications,
setting the foundation for trust and protection.
A prominent example of ethical governance
is the European Union's proposed AI Act,
which categorizes AI systems into various risk levels
and specifies guidelines for each.
For high risk applications, such as facial recognition,
the act requires comprehensive bias testing
to detect and mitigate potential discriminatory outcomes.
Alongside transparency through clear documentation
of data sources, algorithms,
and decision making processes for regulatory audits.
Developers are also expected
to integrate human oversight features,
allowing intervention in the case of errors.
And informing users of system capabilities and limitations.
These aspects of ethical governance
help guide responsible AI deployments,
minimizing unintended harm, and promoting fairness.
Conversely, a failure in ethical governance
is demonstrated by COMPAS,
the Correctional Offender Management
Profiling for Alternative Sanctions.
This is an algorithm used in the United States
to assess recidivism, which is the likelihood
of an individual re-offending after prison release.
Deployed without requirements for bias testing
or transparency, COMPAS operated with limited scrutiny
over its impact on users.
And while it was designed to support
pretrial and parole decisions,
rather than determine sentences directly,
studies revealed that COMPAS tended to overestimate
the risk of recidivism for minority defendants,
leading to racial disparities in outcomes.
In this case, an ethical governance framework
could have mandated bias audits and transparency measures,
allowing stakeholders to monitor and address
any unintended discriminatory patterns
in the algorithm's outputs.
This underscores the importance of governance structures
in high stakes decisions to prevent unfair treatment
stemming from biased data.
Now, for AI governance frameworks to be effective,
they must prioritize transparency and accountability.
Transparency enables users and stakeholders to understand
the AI decision making processes,
especially when these decisions
could affect individual rights.
By setting transparency standards,
organizations can make available clear explanations
of the AI's decision logic, data sources,
and any potential biases,
building trust and ensuring informed AI use.
Accountability is equally as important,
as it establishes responsibility
for the outcomes of AI systems.
With accountability embedded into governance,
organizations have protocols to address errors or biases,
and correct issues properly.
This ensures that if an AI system produces
an inaccurate or a harmful result, there is a clear process
to investigate and prevent future errors,
enabling organizations to manage risks.
And support the ethical deployment
of AI in critical applications.
Second, we have organizational policies on the use of AI.
Organizational policies on AI use establish internal rules
and guidelines to control how AI is used within a company,
ensuring it aligns with ethical standards
and legal obligations.
Microsoft, for example, has AI ethics principles
that support reviews for high impact systems
involving expert oversight,
to help ensure technology is deployed responsibly.
This policy safeguards users from potential harm,
and standardizes responsible AI use
across the organization.
A notable case underscoring the importance
of strong policies is Amazon's discontinued AI hiring tool,
which was found to exhibit gender bias
due to being trained on historical data
that favored male candidates.
This issue highlights why internal policies should mandate
fairness checks to prevent AI from replicating biases,
especially in sensitive areas like hiring
that impact people's careers and livelihoods.
Okay, we have a good and bad example
of organizational policies on AI now.
So, what components make a good one?
Transparency, training and education,
as well as clear guidelines
and continuous training is the answer.
Transparent policies establish clear ethical boundaries,
helping employees understand how AI
will be applied within their roles.
And defining acceptable practices
for data collection and AI applications,
aligned with ethical and privacy standards.
Next, training and education policies ensure employees
are equipped with knowledge about AI ethics.
A structured ethics training program, for instance,
could cover topics like data privacy,
algorithmic bias, and transparency in decision making.
Finally, clear guidelines and continuous training
reinforce accountability at all levels of AI interaction,
ensuring that employees use AI
in ways that align with organizational values.
Understanding both the technical
and ethical frameworks of AI
helps employees engage with the technology responsibly,
building trust in AI as it becomes
more deeply integrated into business processes.
So remember, ethical and governance frameworks
for artificial intelligence, or AI,
ensure the technology is used responsibly.
And aligns with societal and organizational values.
First, ethical governance provides the structure
for deploying AI fairly, transparently, and accountably.
This builds trust and minimizes unintended harm.
Next, organizational policies define specific rules
for how AI is applied with companies, ensuring its use
aligns with both ethical standards and legal requirements.
Finally, transparency and accountability
are key to these frameworks,
helping stakeholders understand AI decisions
and manage risks responsibly.
Together, these approaches support a safe
and ethical integration of AI, fostering a culture
of responsible technology use within organizations.